Distinct and shared contributions of diagnosis and symptom domains to cognitive performance in severe mental illness
BackgroundSevere mental illness (SMI) diagnoses display overlapping symptomatology and shared genetic risk, motivating trans-diagnostic investigations of disease-relevant quantitative measures. We analyzed relationships between neurocognitive performance, symptom domains, and diagnoses, in a large sample of SMI cases (ascertained agnostic to diagnosis) and healthy controls from a single, homogeneous population. Methods2,406 participants (1,689 cases, 717 controls; mean age 39 years, 64% female) were assessed for speed and accuracy using the Penn Computerized Neurocognitive Battery (CNB). Cases carried structured-interview based diagnoses of schizophrenia (SCZ, n=160), bipolar-I (BP-I, n=519), bipolar-II (BP-II, n=204) and major depressive disorder (MDD, n=806). Linear mixed models, using CNB tests as repeated measures, modeled neurocognition as a function of diagnosis, sex, and all interactions. Follow-up analyses, in cases, included symptom factor scores obtained from exploratory factor analysis of symptom data, as main effects. FindingsBP-I and SCZ displayed nearly identical impairments in accuracy and speed, across cognitive domains. BP-II and MDD performed similarly to controls, with subtle deficits in executive and social cognition. A three-factor model (psychosis, mania, and depression) best represented symptom data. Controlling for diagnosis, premorbid IQ, and disease severity, high lifetime psychosis scores were associated with reduced accuracy and speed across cognitive domains, while high depression scores were associated with increased social cognition accuracy. InterpretationTrans-diagnostic investigations demonstrated that neurocognitive function in SMI is characterized by two distinct profiles (BP-I/SCZ and BP-II/MDD), and is associated with specific symptom domains. These results suggest the utility of this design for elucidating SMI causes and trajectories.